{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import absolute_import\n",
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import argparse\n",
    "import sys\n",
    "\n",
    "from tensorflow.examples.tutorials.mnist import input_data\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "FLAGS = None\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz\n",
      "Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz\n"
     ]
    }
   ],
   "source": [
    "# Import data\n",
    "data_dir = '/tmp/tensorflow/mnist/input_data'\n",
    "mnist = input_data.read_data_sets(data_dir, one_hot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#每个批次的大小\n",
    "batch_size=100\n",
    "#计算一共有多少个批次\n",
    "n_batch=mnist.train.num_examples//batch_size\n",
    " \n",
    "#初始化权值\n",
    "def weight_variable(shape):\n",
    "    initial=tf.truncated_normal(shape,stddev=0.1)#生成一个截断的正态分布\n",
    "    return tf.Variable(initial)\n",
    " \n",
    "#初始化偏置\n",
    "def bias_variable(shape):\n",
    "    initial=tf.constant(0.1,shape=shape)\n",
    "    return tf.Variable(initial)\n",
    " \n",
    "#卷积层\n",
    "def conv2d(x,W):\n",
    "    #x input tensor of shape '[batch,in_height,in_width,in_channles]'\n",
    "    #W filter / kernel tensor of shape [filter_height,filter_width,in_channels,out_channels]\n",
    "    #`strides[0] = strides[3] = 1`. strides[1]代表x方向的步长，strides[2]代表y方向的步长\n",
    "    #padding: A `string` from: `\"SAME\", \"VALID\"`\n",
    "    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME')#2d的意思是二维的卷积操作\n",
    " \n",
    "#池化层\n",
    "def max_pool_2x2(x):\n",
    "    #ksize [1,x,y,1]\n",
    "    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(tf.float32, [None, 784])\n",
    "y = tf.placeholder(tf.float32, [None, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#改变x的格式转为4D的向量[batch, in_height, in_width, in_channels]`\n",
    "x_image = tf.reshape(x,[-1,28,28,1])\n",
    " \n",
    "#初始化第一个卷积层的权值和偏置\n",
    "W_conv1 = weight_variable([5,5,1,32])#5*5的采样窗口，32个卷积核从1个平面抽取特征\n",
    "b_conv1 = bias_variable([32])#每一个卷积核一个偏置值\n",
    " \n",
    "#把x_image和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数\n",
    "h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+b_conv1)\n",
    "h_pool1 = max_pool_2x2(h_conv1)#进行max-pooling\n",
    " \n",
    "#初始化第二个卷积层的权值和偏置\n",
    "W_conv2 = weight_variable([5,5,32,64])#5*5的采样窗口，64个卷积核从32个平面抽取特征\n",
    "b_conv2 = bias_variable([64])#每一个卷积核一个偏置值\n",
    " \n",
    "#把h_pool1和权值向量进行卷积，再加上偏置值，然后应用于relu激活函数\n",
    "h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2)\n",
    "h_pool2 = max_pool_2x2(h_conv2)#进行max-pooling\n",
    " \n",
    "#28*28的图片第一次卷积后还是28*28（数组变小了，但是图像大小不变），第一次池化后变为14*14\n",
    "#第二次卷积后为14*14（卷积不会改变平面的大小），第二次池化后变为了7*7\n",
    "#进过上面操作后得到64张7*7的平面\n",
    " \n",
    "#初始化第一个全连接层的权值\n",
    "W_fc1 = weight_variable([7*7*64,1024])#上一层有7*7*64个神经元，全连接层有1024个神经元\n",
    "b_fc1 = bias_variable([1024])#1024个节点\n",
    " \n",
    "#把池化层2的输出扁平化为1维\n",
    "h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])\n",
    "#求第一个全连接层的输出\n",
    "h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1) + b_fc1)\n",
    " \n",
    "#keep_prob用来表示神经元的输出概率\n",
    "keep_prob = tf.placeholder(tf.float32)\n",
    "h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)\n",
    " \n",
    "#初始化第二个全连接层\n",
    "W_fc2 = weight_variable([1024,10])\n",
    "b_fc2 = bias_variable([10])\n",
    " \n",
    "#计算输出\n",
    "prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=prediction))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)\n",
    "\n",
    "train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "correct_prediction = tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))#argmax返回一维张量中最大的值所在的位置\n",
    " \n",
    "#求准确率\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))\n",
    " \n",
    "with tf.Session() as sess:\n",
    "    sess.run(tf.global_variables_initializer())\n",
    "    for epoch in range(21):\n",
    "        for batch in range(n_batch):\n",
    "            batch_xs,batch_ys =  mnist.train.next_batch(batch_size)\n",
    "            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys,keep_prob:0.7})\n",
    " \n",
    "        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels,keep_prob:1.0})\n",
    "        print(\"Iter \"+str(epoch)+\", Testing Accuracy= \"+str(acc))\n",
    "        \n",
    "correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))\n",
    "accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n",
    "print(sess.run(accuracy, feed_dict={x: mnist.test.images,\n",
    "                                      y_: mnist.test.labels}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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